High-Precision Damage Imaging Driven by High-Efficiency Sparse Measurement: A Synergistic Detection Framework for Guided Wavefield Reconstruction and Feature Enhancement

To address low data acquisition efficiency in full-wavefield data collection for guided-wave-based structural damage detection, the limited adaptability of conventional single-domain sparse reconstruction methods, and insufficient imaging accuracy, this paper proposes a damage detection framework that integrates joint wavenumber-space domain sparse reconstruction with damage-sensitive mode gradient enhancement (JWSSR-SMGE). A joint wavenumber-space domain sparse reconstruction (JWSSR) model is established, which incorporates two weighting strategies, namely offline-calibrated ADMM (OC-ADMM) and iteratively adaptive weighted ADMM (IAW-ADMM). By simultaneously exploiting the modal sparsity in the wavenumber domain and the damage-induced sparsity in the spatial domain, the proposed model achieves high-accuracy and robust reconstruction under various damage characteristics. To address the issues of noise amplification and weak-component attenuation in the reconstructed signals, a damage-sensitive mode gradient enhancement (SMGE) imaging method is further designed. This method employs annular filtering and gradient-based weighting strategies to achieve balanced enhancement of damage-related responses across different wavenumber components, thereby effectively suppressing background noise and highlighting damage features. Both numerical simulations and experimental results demonstrate that under sparse sampling conditions with a sampling compression ratio as low as 30%, the total inspection time is reduced by more than 58% compared with full sampling. The reconstruction accuracy of the proposed method is improved by 6%--21% over conventional single-domain approaches. The imaging error of the SMGE-based method is generally lower than that of the conventional sensitive mode enhancement (SME) method, thereby achieving integrated detection from high-efficiency sparse measurement to high-precision damage imaging.